where \([\cdot ]_+\)  denotes the positive part, and \(\alpha \)  is the margin. We use \((a, p, n) \in {T} \circ B\)  as the triplets sampled from the current mini-batch transformed with given domain augmentation \({T}\) . The triplet loss strives to keep the anchor point \(a\)  closer to the positive data point \(p\)  than the negative point \(n\)  by at least a margin \(\alpha \) . For brevity, we omit the definitions of contrastive loss and MS loss, but we refer the interested reader to the original works [1]}, [2]}.
